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This paper investigates the long term drift phenomenon affecting electrochemical sensors used in real environmental conditions to monitor the nitrogen dioxide concentration [NO2]. Electrochemical sensors are low-cost gas sensors able to detect pollutant gas at part per billion level and may be employed to enhance the air quality monitoring networks. However, they suffer from many forms of drift caused by climatic parameter variations, interfering gases and aging. Therefore, they require frequent, expensive and time-consuming calibrations, which constitute the main obstacle to the exploitation of these kinds of sensors. This paper proposes an empirical, linear and unsupervised drift correction model, allowing to extend the time between two successive full calibrations. First, a calibration model is established based on multiple linear regression. The influence of the air temperature and humidity is considered. Then, a correction model is proposed to solve the drift related to age issue. The slope and the intercept of the correction model compensate the change over time of the sensors’ sensitivity and baseline, respectively. The parameters of the correction model are identified using particle swarm optimization (PSO). Data considered in this work are continuously collected onsite close to a highway crossing Metz City (France) during a period of 6 months (July to December 2018) covering almost all the climatic conditions in this region. Experimental results show that the suggested correction model allows maintaining an adequate [NO2] estimation accuracy for at least 3 consecutive months without needing any labeled data for the recalibration.
Rachid Laref; Etienne Losson; Alexandre Sava; Maryam Siadat. Empiric Unsupervised Drifts Correction Method of Electrochemical Sensors for in Field Nitrogen Dioxide Monitoring. Sensors 2021, 21, 3581 .
AMA StyleRachid Laref, Etienne Losson, Alexandre Sava, Maryam Siadat. Empiric Unsupervised Drifts Correction Method of Electrochemical Sensors for in Field Nitrogen Dioxide Monitoring. Sensors. 2021; 21 (11):3581.
Chicago/Turabian StyleRachid Laref; Etienne Losson; Alexandre Sava; Maryam Siadat. 2021. "Empiric Unsupervised Drifts Correction Method of Electrochemical Sensors for in Field Nitrogen Dioxide Monitoring." Sensors 21, no. 11: 3581.
Rachid Laref; Etienne Losson; Alexandre Sava; Maryam Siadat. Field Evaluation of Low Cost Sensors Array for Air Pollution Monitoring. 2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) 2019, 1 .
AMA StyleRachid Laref, Etienne Losson, Alexandre Sava, Maryam Siadat. Field Evaluation of Low Cost Sensors Array for Air Pollution Monitoring. 2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS). 2019; ():1.
Chicago/Turabian StyleRachid Laref; Etienne Losson; Alexandre Sava; Maryam Siadat. 2019. "Field Evaluation of Low Cost Sensors Array for Air Pollution Monitoring." 2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) , no. : 1.
Support Vector Machine Regression (SVR) has been shown to be more accurate compared to other machine learning techniques that are commonly used for chemical sensors arrays applications. However, the performance of SVR depends strongly on the selection of its hyperparameters. Most of time, researchers in this field rely on trivial grid search methods to find suitable values of SVR hyperparameters by minimizing the cross-validation prediction error. This method is not a practical solution because of the large domain of possible parameter values, which is further exacerbated by the lack of prior knowledge on the data. In this article, we investigate the optimization of SVR hyperparameters by combining the SVR algorithm with a simple algorithm for SVR parameters selection. We begin by studying the influence of each hyperparameter on SVR performance. We then propose the Generalized Pattern Search algorithm (GPS) as a faster alternative to determine these hyperparameters. Finally, we demonstrate that the proposed GPS algorithm, with its simplicity and robustness, gives similar results compared to more complicated alternatives, such as Genetic Algorithms, Simulating Annealing, Bayesian Optimization or Particle Swarm Optimization.
R. Laref; Etienne Losson; A. Sava; M. Siadat. On the optimization of the support vector machine regression hyperparameters setting for gas sensors array applications. Chemometrics and Intelligent Laboratory Systems 2018, 184, 22 -27.
AMA StyleR. Laref, Etienne Losson, A. Sava, M. Siadat. On the optimization of the support vector machine regression hyperparameters setting for gas sensors array applications. Chemometrics and Intelligent Laboratory Systems. 2018; 184 ():22-27.
Chicago/Turabian StyleR. Laref; Etienne Losson; A. Sava; M. Siadat. 2018. "On the optimization of the support vector machine regression hyperparameters setting for gas sensors array applications." Chemometrics and Intelligent Laboratory Systems 184, no. : 22-27.
Recently, the emergence of low-cost sensors have allowed electronic noses to be considered for densifying the actual air pollution monitoring networks in urban areas. Electronic noses are affected by changes in environmental conditions and sensor drifts over time. Therefore, they need to be calibrated periodically and also individually because the characteristics of identical sensors are slightly different. For these reasons, the calibration process has become very expensive and time consuming. To cope with these drawbacks, calibration transfer between systems constitutes a satisfactory alternative. Among them, direct standardization shows good efficiency for calibration transfer. In this paper, we propose to improve this method by using kernel SPXY (sample set partitioning based on joint x-y distances) for data selection and support vector machine regression to match between electronic noses. The calibration transfer approach introduced in this paper was tested using two identical electronic noses dedicated to monitoring nitrogen dioxide. Experimental results show that our method gave the highest efficiency compared to classical direct standardization.
Rachid Laref; Etienne Losson; Alexandre Sava; Maryam Siadat. Support Vector Machine Regression for Calibration Transfer between Electronic Noses Dedicated to Air Pollution Monitoring. Sensors 2018, 18, 3716 .
AMA StyleRachid Laref, Etienne Losson, Alexandre Sava, Maryam Siadat. Support Vector Machine Regression for Calibration Transfer between Electronic Noses Dedicated to Air Pollution Monitoring. Sensors. 2018; 18 (11):3716.
Chicago/Turabian StyleRachid Laref; Etienne Losson; Alexandre Sava; Maryam Siadat. 2018. "Support Vector Machine Regression for Calibration Transfer between Electronic Noses Dedicated to Air Pollution Monitoring." Sensors 18, no. 11: 3716.
The present paper deals with gas concentration monitoring based on an electronic nose. The proposed approach investigates two regression methods for gas concentration estimation: the first is the most used in gas quantification with electronic nose and known as the partial least squares (PLS) and the second, known as the support vector machine (SVM) regression, is recently used by the electronic nose community. Data used in this work are collected using an E-nose device developed in our laboratory and responding to various concentrations of pine essential oil vapours. The comparison between the two regression methods studied in this paper is related to the accuracy, the universality as well as the number of samples needed for learning. The results are analyzed in order to select the more suitable prediction model for gas concentration estimation.
R. Laref; E. Losson; A. Sava; Kondo Hloindo Adjallah; M. Siadat. A comparison between SVM and PLS for E-nose based gas concentration monitoring. 2018 IEEE International Conference on Industrial Technology (ICIT) 2018, 1335 -1339.
AMA StyleR. Laref, E. Losson, A. Sava, Kondo Hloindo Adjallah, M. Siadat. A comparison between SVM and PLS for E-nose based gas concentration monitoring. 2018 IEEE International Conference on Industrial Technology (ICIT). 2018; ():1335-1339.
Chicago/Turabian StyleR. Laref; E. Losson; A. Sava; Kondo Hloindo Adjallah; M. Siadat. 2018. "A comparison between SVM and PLS for E-nose based gas concentration monitoring." 2018 IEEE International Conference on Industrial Technology (ICIT) , no. : 1335-1339.
Metal oxide sensors are the most often used in electronic nose devices because of their high sensitivity, long lifetime, and low cost. However, these sensors suffer from a lack of response stability making the electronic nose systems useless in industrial applications. The sensor instabilities are particularly caused by incomplete recovery process producing gradual drifts in the sensor responses. This paper focuses on a signal processing method combining baseline manipulation and orthogonal signal correction technique in order to reduce effectively the drift impact from the sensor outputs. The proposed signal processing is explored using experimental data obtained from a gas sensor array responding to various concentrations of pine essential oil vapors. Partial Least Square method is then applied on the corrected dataset to establish a regression model for the estimation of gas concentration. In this work, we show essentially how our drift correction approach can help to improve significantly the stability of the regression model, while ensuring good accuracy.
Rachid Laref; Diaa Ahmadou; Etienne Losson; Maryam Siadat. Orthogonal Signal Correction to Improve Stability Regression Model in Gas Sensor Systems. Journal of Sensors 2017, 2017, 1 -8.
AMA StyleRachid Laref, Diaa Ahmadou, Etienne Losson, Maryam Siadat. Orthogonal Signal Correction to Improve Stability Regression Model in Gas Sensor Systems. Journal of Sensors. 2017; 2017 ():1-8.
Chicago/Turabian StyleRachid Laref; Diaa Ahmadou; Etienne Losson; Maryam Siadat. 2017. "Orthogonal Signal Correction to Improve Stability Regression Model in Gas Sensor Systems." Journal of Sensors 2017, no. : 1-8.
Metal-oxide gas sensors are largely used in electronic nose devices thanks to their high sensitivity and low cost. But for a reliable utilization of these sensors it is imperative to deal with their sensitive element alteration which is produced by gas exposures. In order to limit significantly this alteration, a very time-consuming cleaning process is necessary after each gas exposure. In this way, when continuous and rapid monitoring of a gas concentration is desired, the slow recovery process (tens of minutes) limits strongly the use of an electronic nose composed of MOX sensors. We present here, a signal processing method to reduce the impact of the gas sensor response instabilities resulted from an incomplete cleaning process of their sensitive layer. The proposed technique is based on the correction of sensor responses instabilities, called short-term drifts, by taking into account information coming from the state of the sensor regeneration. This new approach is explored using experimental data from a gas sensor array responding to various concentrations of pine essential oil vapors. The proposed signal processing technique significantly limits the short-term drift effects on the gas sensor responses. Comparing to classical methods used for drift correction, our approach shows an improved intensity discrimination of the odorant atmospheres. We hope that this study can help industrials or researchers working on the field of electronic nose development concerning the drift compensation of MOX gas sensors.
D. Ahmadou; R. Laref; Etienne Losson; M. Siadat. Reduction of drift impact in gas sensor response to improve quantitative odor analysis. 2017 IEEE International Conference on Industrial Technology (ICIT) 2017, 928 -933.
AMA StyleD. Ahmadou, R. Laref, Etienne Losson, M. Siadat. Reduction of drift impact in gas sensor response to improve quantitative odor analysis. 2017 IEEE International Conference on Industrial Technology (ICIT). 2017; ():928-933.
Chicago/Turabian StyleD. Ahmadou; R. Laref; Etienne Losson; M. Siadat. 2017. "Reduction of drift impact in gas sensor response to improve quantitative odor analysis." 2017 IEEE International Conference on Industrial Technology (ICIT) , no. : 928-933.